See the mechanism
Every entry leads with a visual. Coding traces expose the state, move, and invariant instead of asking you to memorize a solution.
mlmentorship field guide2026 edition
Visual-first · senior to principal
The ML Interview Field Guide
A visual book for building the mechanisms, judgment, and answer depth expected across senior Applied Scientist, Research Scientist, Machine Learning Engineer, and Research Engineer interviews.
- 390 visual lessonsmechanisms before summaries
- 191 questionspractice and answer calibration
- 106 coding tracesstate, move, and invariant
- 10 ordered booksfoundations through execution
Free to read · no account required · progress stays in this browser
Start here
Choose the shortest route from what you already know.
You do not need to read the library front to back.
Active interview loop? Build a private plan from your role, rounds, available time, and recent evidence. It stays in this browser.
The curriculum
10 books, ordered from foundations to interview execution.
Open a book to inspect its chapters, or start reading its first entry now.
Core ML
Build the technical base used across applied, research, and engineering interviews.
- I
ML foundations
Math, probability, classical machine learning, deep learning, and the core questions that test them.
9 chapters · 62 entries - II
Model training and research
Optimization, reliable experiments, implementation, debugging, and research judgment.
9 chapters · 52 entries - III
Evaluation and product ML
Metrics, experimental validity, calibration, product decisions, and production evaluation.
4 chapters · 25 entries
Frontier AI systems
Study language models, post-training, agents, accelerators, and distributed systems.
- IV
LLMs, agents, and post-training
Transformer internals, inference, retrieval, evaluation, agents, alignment, and post-training.
7 chapters · 42 entries - V
ML systems and infrastructure
Accelerators, distributed training, inference systems, reliability, cost, and full ML architecture.
6 chapters · 30 entries
Specialist tracks
Add only the specialist subject required by the role and team.
- VI
Retrieval, ranking, and recommendations
Embeddings, candidate generation, ranking, search metrics, cold start, and feedback loops.
4 chapters · 23 entries - VII
Reinforcement learning and robotics
Sequential decisions, value and policy methods, environments, rewards, and robotics policy learning.
3 chapters · 13 entries - VIII
Vision, language, and speech
Visual models, multimodal systems, sequence modeling, natural language, and speech.
4 chapters · 21 entries
Interview execution
Prepare role choice, project evidence, behavioral judgment, and senior-level communication.
- IX
Interview and career practice
Role choice, level calibration, project stories, behavioral judgment, and long-form field guides.
3 chapters · 15 entries - X
Coding interview practice
A visual-first coding field guide for data structures, algorithms, and practical AI coding. Learn each problem by seeing the state it preserves, the move it makes, and the invariant that makes the move safe.
11 chapters · 107 entries
Why mlmentorship
Interview performance, not passive completion.
Practice the round
Questions cover coding, math, ML breadth, system design, research, product, project, behavioral, and strategy interviews.
Calibrate the depth
Answers separate reliable execution, senior ownership, staff architecture, principal judgment, and company-dependent upper-IC scope.
Built for AS, RS, MLE, and RE candidates. Generic algorithms, SQL, and backend curricula remain external.
Inspect the depth
All design questions The design cases connect technical choices to evidence and ownership.
Built from practice
Specific, current, and honest about scope.
Written by Hamidreza Saghir, Principal Applied Scientist at Microsoft, with earlier ML engineering, applied-science, and research roles at X, Amazon, and Borealis AI. The site uses public process evidence, never leaked prompts or job-outcome promises. About the author and project.